MAiD, Mental Disorder, and Capacity: Recognizing the Complexity of Moral Agency in Capacity Assessment
Bibliographic record
Abstract
Medical assistance in dying (MAiD) has become a prominent form of end-of-life care within the Canadian health system, yet it is not without its critics. Drawing even more critical attention is the possibility of Canada expanding MAiD eligibility to persons who suffer from mental disorder as their sole underlying medical condition (MAiD MD-SUMC). Unlike physical conditions that cause pain and suffering, mental disorder has the intrinsic potential to affect one’s ability to understand and appropriately value the consequences of one’s actions and decisions. There is thus a significant risk that a patient who has requested MAiD MD-SUMC may be unable to provide valid consent due to an impaired ability to either: 1) adequately understand the consequences of receiving MAiD or 2) place that consequence within a consistent set of values. Due to the important ways in which mental disorder can affect one’s values and desires, this paper argues that we must evaluate decision-making capacity in a more holistic way that includes both cognitive and evaluative factors. My argument is based upon a presentation of the evaluative factors involved in decision-making, a demonstration that these factors may be significantly affected by mental illness, and a suggestion that we require more holistic criteria for capacity evaluation than the excessively cognitive criteria espoused by most commonly used assessment tools. Because of the interplay between these aspects of my argument and the extremely high stakes involved in MAiD assessments, I suggest that capacity evaluations (both in general and especially for MAiD requests) ought to incorporate an aspect of narrative assessment by which the patient’s values and self-understanding can be better assessed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".